Pain management based on respiration-mediated heart rates

Abstract

This document discusses, among other things, systems and methods for managing pain of a subject. A system may include a sensor circuit configured to sense a respiration signal and a heart rate signal. A pain analyzer circuit may determine respiratory cycles and respiratory phases in a respiratory cycle, and generate one or more signal metrics indicative of respiration-mediated heart rate variation. The pain analyzer may generate a pain score using the signal metrics indicative of respiration-mediated heart rate variation. The pain score may be output to a user or a process. The system may include an electrostimulator to generate and deliver closed-loop pain therapy according to the pain score.

Description

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 62/445,069, filed on Jan. 11, 2017, which is herein incorporated by reference in its entirety.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,053 entitled “PAIN MANAGEMENT USING CARDIOVASCULAR PARAMETERS”, filed on January 11, U.S. Provisional Patent Application Ser. No. 62/445,061, entitled “PAIN MANAGEMENT BASED ON BRAIN ACTIVITY MONITORING”, filed on Jan. 11, 2017, U.S. Provisional Patent Application Ser. No. 62/445,075, entitled “PAIN MANAGEMENT BASED ON FUNCTIONAL MEASUREMENTS”, filed on Jan. 11, 2017, U.S. Provisional Patent Application Ser. No. 62/445,082, entitled “PAIN MANAGEMENT BASED ON EMOTIONAL EXPRESSION MEASUREMENTS”, filed on Jan. 11, 2017, U.S. Provisional Patent Application Ser. No. 62/445,092, entitled “PAIN MANAGEMENT BASED ON MUSCLE TENSION MEASUREMENTS”, filed on Jan. 11, 2017, U.S. Provisional Patent Application Ser. No. 62/445,095, entitled “PATIENT-SPECIFIC CALIBRATION OF PAIN QUANTIFICATION”, filed on Jan. 11, 2017, U.S. Provisional Patent Application Ser. No. 62/395,641, entitled “METHOD AND APPARATUS FOR PAIN MANAGEMENT USING HEART SOUNDS”, filed on Sep. 16, 2016, U.S. Provisional Patent Application Ser. No. 62/400,313, entitled “SYSTEMS AND METHODS FOR CLOSED-LOOP PAIN MANAGEMENT”, filed on Sep. 27, 2016, U.S. Provisional Patent Application Ser. No. 62/400,336, entitled “METHOD AND APPARATUS FOR PAIN MANAGEMENT USING OBJECTIVE PAIN MEASURE”, filed on Sep. 27, 2016, U.S. Provisional Patent Application Ser. No. 62/412,587, entitled “METHOD AND APPARATUS FOR PAIN CONTROL USING BAROREFLEX SENSITIVITY DURING POSTURE CHANGE”, filed on Oct. 25, 2016, which are incorporated by reference in their entirety.

TECHNICAL FIELD

This document relates generally to medical systems and more particularly to systems, devices, and methods for pain management.

BACKGROUND

Pain is one of the most common and among the most personally compelling reasons for seeking medical attention, and consumes considerable healthcare resources each year. The relation between etiology, underlying mechanisms and the specific symptoms and signs related to painful disorders is complex. Pain in an individual patient may be produced by more than one mechanism.

Chronic pain, such as pain present most of the time for a period of six months or longer during the prior year, is a highly pervasive complaint and consistently associated with psychological illness. Chronic pain may originate with a trauma, injury or infection, or there may be an ongoing cause of pain. Chronic pain may also present in the absence of any past injury or evidence of body damage. Common chronic pain can include headache, low back pain, cancer pain, arthritis pain, neurogenic pain (pain resulting from damage to the peripheral nerves or to the central nervous system), or psychogenic pain (pain not due to past disease or injury or any visible sign of damage inside or outside the nervous system).

Chronic pain may be treated or alleviated using medications, acupuncture, surgery, and neuromodulation therapy such as local electrical stimulation or brain stimulation, among others. Examples of neuromodulation include Spinal Cord Stimulation (SCS), Deep Brain Stimulation (DBS), Peripheral Nerve Stimulation (PNS), and Functional Electrical Stimulation (FES). Implantable neuromodulation systems have been applied to deliver such a therapy. An implantable neuromodulation system may include an implantable neurostimulator, also referred to as an implantable pulse generator (IPG), which can electrically stimulate tissue or nerve centers to treat nervous or muscular disorders. In an example, an IPG can deliver electrical pulses to a specific region in a spinal cord, such as particular spinal nerve roots or nerve bundles, to create an analgesic effect that masks pain sensation.

SUMMARY

By way of example, chronic pain management may involve determining appropriate treatment regimens such as SCS and evaluating therapy efficacy. Accurate pain assessment and characterization are desirable for managing patients with chronic pain. Currently, pain assessment generally relies on patient subjective report of pain symptoms, including severity, pattern, or duration of pain. Based on the patient reported pain sensation, a clinician may prescribe a pain therapy, such as to manually program an electrostimulator for delivering a neuromodulation therapy. However, the subjective description of pain sensation may be constrained by patient cognitive abilities. The subjective pain description may also be subject to intra-patient variation, such as due to a progression of a chronic disease, or a change in general health status or medication. Having a patient to report and describe each pain episode he or she has experienced is not efficient and may delay appropriate pain therapy. Additionally, for patients in an ambulatory setting who lack immediate access to medical assistance, manual adjustment of pain therapy by a clinician may not be feasible especially if immediate therapy titration is required. The present inventors have recognized that there remains a demand for improving pain management, such as systems and methods for objective pain assessment and automated closed-loop pain therapy based on objective pain assessment.

This document discusses, among other things, systems, devices, and methods for assessing pain of a subject. The system includes one or more physiological sensors configured to sense a respiration signal and a heart rate signal from the patient. The system may generate one or more signal metrics indicative of respiration-mediated heart rate variation (RM-HRV) using the respiration signal and the heart rate signal. The RM-HRV may indicate autonomic imbalance which is associated with pain. The system may generate a pain score using the one or more signal metrics. The pain score can be output to a patient or used for closed-loop control of a pain therapy.

Example 1 is a system for managing pain of a patient. The system comprises a sensor circuit, a pain analyzer circuit, and an output unit. The sensor circuit may be coupled to one or more physiological sensors and configured to sense a respiration signal and a heart rate signal from the patient. The pain analyzer circuit may be coupled to the sensor circuit and configured to generate one or more signal metrics indicative of respiration-mediated heart rate variation using the sensed respiration signal and the heart rate signal, and generate a pain score using the generated one or more signal metrics indicative of respiration-mediated heart rate variation. The output unit may be configured to output the pain score to a user or a process.

In Example 2, the subject matter of Example 1 optionally includes an electrostimulator that may be configured to generate electrostimulation energy to treat pain, and a controller circuit coupled to the pain analyzer circuit and the electrostimulator. The controller circuit may be configured to control the electrostimulator to deliver a pain therapy and to control the electrostimulation energy generated by the electrostimulator according to the pain score.

In Example 3, the subject matter of Example 2 optionally includes the electrostimulator that may be further configured to deliver at least one of: a spinal cord stimulation; a brain stimulation; or a peripheral nerve stimulation.

In Example 4, the subject matter of any one or more of Examples 2-3 optionally includes the controller circuit that may be further configured to deliver first electrostimulation to the patient in response to the pain score exceeding a threshold value, and to deliver second electrostimulation to the patient in response to the pain score falling below the threshold value. The first electrostimulation may differ from the second electrostimulation with respect to at least one of an electrostimulation energy, an electrostimulation pulse shape, or an electrostimulation pattern.

In Example 5, the subject matter of any one or more of Examples 1-4 optionally includes a respiratory phase detector that may be configured to detect a plurality of respiratory cycles and at least one of an inspiration phase or an expiration phase in each of the plurality of respiratory cycles. The pain analyzer circuit may be configured to synchronize heart rates from the heart rate signal to the plurality of respiratory cycles, and determine the respiration-mediated heart rate variation using the synchronized heart rates.

In Example 6, the subject matter of Example 5 optionally includes the respiration-mediated heart rate variation that may indicate a difference between heart rates during the inspiration phase and heart rates during the expiration phase.

In Example 7, the subject matter of Example 5 optionally includes the respiration-mediated heart rate variation that may indicate a variability of the synchronized heart rates within one or more of the plurality of respiratory cycles.

In Example 8, the subject matter of Example 5 optionally includes the respiration-mediated heart rate variation that may include at least one of: a heart rate variability during the inspiration phase; a heart rate variability during the expiration phase; or a difference between the heart rate variability during the inspiration phase and the heart rate variability during the expiration phase.

In Example 9, the subject matter of any one or more of Examples 1-8 optionally includes the one or more physiological sensors that may include a first sensor and a second sensor different from the first sensor. The sensor circuit may be further configured to sense the respiration signal via the first sensor, and to sense the heart rate signal via the second sensor.

In Example 10, the subject matter of any one or more of Examples 1-9 optionally includes the sensor circuit that may be further configured to: sense a physiological signal via at least one of the one or more physiological sensors; and filter the sensed physiological signal, via a first filter circuit, to generate the respiration signal, or filter the sensed physiological signal, via a second filter circuit, to generate the heart rate signal. The second filter circuit may have a higher center frequency than the first filter circuit.

In Example 11, the subject matter of Example 10 optionally includes the sensor circuit that may be configured to sense the physiological signal including at least one of: a cardiac electrical signal; a heart sound signal; a blood pressure signal; a blood flow signal; a tissue strain signal; or a tissue impedance signal.

In Example 12, the subject matter of any one or more of Examples 1-11 optionally includes the pain analyzer circuit that may be further configured to generate the pain score using a plurality of the one or more signal metrics each weighted by their respective weight factor.

In Example 13, the subject matter of any one or more of Examples 1-12 optionally includes the pain analyzer circuit that may be further configured to generate the pain score using a combination of comparisons between a plurality of the one or more signal metrics and their respective threshold value.

In Example 14, the subject matter of any one or more of Examples 1-13 optionally includes the output unit that may be further configured to produce an alert based on the generated pain score.

In Example 15, the subject matter of Example 2 optionally includes an implantable neuromodulator device (IND) that includes one or more of the sensor circuit, the pain analyzer circuit, or the electrostimulator.

Example 16 is a method for managing pain of a patient using an implantable neuromodulator device (IND). The method comprises steps of: sensing a respiration signal and a heart rate signal from the patient via a sensor circuit; generating one or more signal metrics indicative of respiration-mediated heart rate variation using the sensed respiration signal and the heart rate signal; generating a pain score based on the generated one or more signal metrics indicative of respiration-mediated heart rate variation; and outputting the pain score to a user or a process.

In Example 17, the subject matter of Example 16 optionally includes delivering a pain therapy via the IND. The pain therapy may include electrostimulation energy determined according to the pain score.

In Example 18, the subject matter of claim 16 optionally includes detecting a plurality of respiratory cycles and at least one of an inspiration phase or an expiration phase in each of the plurality of respiratory cycles, synchronizing heart rates from the heart rate signal to the plurality of respiratory cycles, and determining the respiration-mediated heart rate variation using the synchronized heart rates.

In Example 19, the subject matter of Example 18 optionally includes the respiration-mediated heart rate variation that may indicate a difference between heart rates during the inspiration phase and heart rates during the expiration phase.

In Example 20, the subject matter of Example 18 optionally includes the respiration-mediated heart rate variation that may indicate a variability of the synchronized heart rates within one or more of the plurality of respiratory cycles.

In Example 21, the subject matter of Example 18 optionally includes the respiration-mediated heart rate variation that may include at least one of: a heart rate variability during the inspiration phase; a heart rate variability during the expiration phase; or a difference between the heart rate variability during the inspiration phase and the heart rate variability during the expiration phase.

In Example 22, the subject matter of Example 16 optionally includes sensing the respiration signal and the heart rate signal that may include steps of: sensing a physiological signal using a physiological sensor; and filtering the sensed physiological signal, via a first filter circuit, to generate the respiration signal, or filtering the sensed physiological signal, via a second filter circuit, to generate the heart rate signal. The second filter circuit may have a higher center frequency than the first filter circuit.

In Example 23, the subject matter of Example 16 optionally includes generating the pain score that may include using a plurality of the one or more signal metrics each weighted by their respective weight factor.

Sensor-based pain scores using the respiration-mediated heart rates as discussed in this document may improve automated assessment of patient pain, as well as individualized therapies to alleviate pain or to reduce side effects. The systems, devices, and methods discussed in this document may also enhance the performance and functionality of a pain management system or device. A device or a system programmed with the sensor-based pain assessment methods can have improved automaticity in medical diagnostics. More efficient device memory or communication bandwidth usage may be achieved by storing or transmitting medical information more relevant to clinical decisions. Additionally, through improved pain therapy efficacy based on patient individual need, battery longevity of an implantable device may be enhanced, or pain medication volume may be saved.

This summary is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the disclosure. The detailed description is included to provide further information about the present patent application. Other aspects of the disclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures of the accompanying drawings. Such embodiments are demonstrative and not intended to be exhaustive or exclusive embodiments of the present subject matter.

FIG. 1 illustrates, by way of example and not limitation, a neuromodulation system and portions of an environment in which the neuromodulation system may operate.

FIG. 2 illustrates, by way of example and not limitation, a block diagram of a pain management system.

FIG. 3 illustrates, by way of example and not limitation, a block diagram of a pain management system comprising an implantable neuromodulator.

FIGS. 4A-B illustrate, by way of example and not limitation, diagrams illustrating a comparison of respiration-mediated heart rates in a pain state and respiration-mediated heart rates in a pain-free state.

FIG. 5 illustrates, by way of example and not limitation, a flow chart of a method for managing pain in a patient.

FIG. 6 illustrates, by way of example of not limitation, a block diagram of an example machine upon which any one or more of the techniques discussed herein may perform.

DETAILED DESCRIPTION

In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the spirit and scope of the present invention. References to “an”, “one”, or “various” embodiments in this disclosure are not necessarily to the same embodiment, and such references contemplate more than one embodiment. The following detailed description provides examples, and the scope of the present invention is defined by the appended claims and their legal equivalents.

Clinically, chronic pain may be associated with changes in patient autonomic balance. Painful states may impair neuro-cardiac integrity. In the presence of pain, elevated sympathetic activity and/or withdrawal of parasympathetic activity may cause an increase in heart rate (HR) and decrease in heart rate variability (HRV). Conversely, when pain subsides such as via an effective pain therapy, parasympathetic activation may increase to restore autonomic balance, resulting in a decrease in HR and an increase in HRV. A short-term measure of HRV is respiratory sinus arrhythmia (RSA) that represents a cardiorespiratory coupling, which may also be affected by autonomic balance. The RSA may be related to baroreceptor activation due to changes in arterial pressure as a result of inspiratory increase in venous return to the heart. Parasympathetic activity is withdrawn during inspiration, resulting in an increase in HR and decrease in HRV. During expiration, parasympathetic activity increases, which causes HR to decrease and HRV to increase. Therefore, monitoring of patient respiration-mediated heart rates may provide an objective assessment of pain, and may be used to improve pain therapy efficacy.

Disclosed herein are systems, devices, and methods for or assessing pain of a subject, and optionally programming pain therapy based on the pain assessment. In various embodiments, the present system may include sensors configured to sense a respiration signal and a heart rate signal. A pain analyzer circuit may generate a pain score using signal metrics indicative of respiration-mediated heart rate variation. The system may include a neurostimulator that can deliver a pain therapy according to the pain score.

The present system may be implemented using a combination of hardware and software designed to provide a closed-loop pain management regimen to increase therapeutic efficacy, increase patient satisfaction for neurostimulation therapies, reduce side effects, and/or increase device longevity. The present system may be applied in any neurostimulation (neuromodulation) therapies, including but not limited to SCS, DBS, PNS, FES, motor cortex stimulation, sacral nerve stimulation, and vagus nerve stimulation (VNS) therapies. In various examples, instead of providing closed-loop pain therapies, the systems, devices, and methods described herein may be used to monitor the patient and assess pain that either occurs intrinsically or is induced by nerve block procedures or radiofrequency ablation therapies, or side effects like paresthesia caused by the stimulation therapy, among others. The patient monitoring may include generating recommendations to the patient or a clinician regarding pain treatment.

FIG. 1 illustrates, by way of example and not limitation, a neuromodulation system 100 for managing pain of a subject such as a patient with chronic pain, and portions of an environment in which the neuromodulation system 100 may operate. The neuromodulation system 100 may include an implantable system 110 that may be associated with a body 199 of the subject, and an external system 130 in communication with the implantable system 110 via a communication link 120.

The implantable system 110 may include an ambulatory medical device (AMD), such as an implantable neuromodulator device (IND) 112, a lead system 114, and one or more electrodes 116. The IND 112 may be configured for subcutaneous implant in a patient's chest, abdomen, upper gluteal surface, or other parts of the body 199. The IND 112 may be configured as a monitoring and diagnostic device. The IND 112 may include a hermetically sealed can that houses sensing circuitry to sense physiological signals from the patient via sensing electrodes or ambulatory sensors associated with the patient and in communication with the IND 112, such as the one or more electrodes 116. In some examples, the sensing electrodes or the ambulatory sensors may be included within the IND 112. The physiological signals, when measured during a pain episode, may be correlative to severity of the pain. In an example, the IND 112 may sense, via sensors or electrodes, a respiration signal and a heart rate signal, and generate one or more signal metrics indicative of respiration-mediated heart rate variation (RM-HRV) that represents variability of heart rate (or cardiac cycle length) modulated by respiration, such as heart rate variability at different phases of respiration. The RM-HRV indicates autonomic imbalance which is associated with pain. The IND 112 may characterize and quantify the pain based on the sensed physiological signals, such as to determine onset, intensity, severity, duration, or patterns of the pain experienced by the subject. The IND 112 may generate an alert to indicate occurrence of a pain episode, pain exacerbation, or efficacy of pain therapy, and present the alert to a clinician.

The IND 112 may alternatively be configured as a therapeutic device for treating or alleviating the pain. In addition to the pain monitoring circuitry, the IND 112 may further include a therapy unit that can generate and deliver energy or modulation agents to a target tissue. The energy may include electrical, magnetic, thermal, or other types of energy. In some examples, the IND 112 may include a drug delivery system such as a drug infusion pump that can deliver pain medication to the patient, such as morphine sulfate or ziconotide, among others.

The IND 112 may include electrostimulation circuitry that generates electrostimulation pulses to stimulate a neural target via the electrodes 116 operably connected to the IND 112. In an example, the electrodes 116 may be positioned on or near a spinal cord, and the electrostimulation circuitry may be configured to deliver SCS to treat pain. In another example, the electrodes 116 may be surgically placed at other neural targets such as a brain or a peripheral neutral tissue, and the electrostimulation circuitry may be configured to deliver brain or peripheral stimulations. Examples of electrostimulation may include deep brain stimulation (DBS), trigeminal nerve stimulation, occipital nerve stimulation, vagus nerve stimulation (VNS), sacral nerve stimulation, sphenopalatine ganglion stimulation, sympathetic nerve modulation, adrenal gland modulation, baroreceptor stimulation, or transcranial magnetic stimulation, spinal cord stimulation (SCS), dorsal root ganglia (DRG) stimulation, motor cortex stimulation (MCS), transcranial direct current stimulation (tDCS), transcutaneous spinal direct current stimulation (tsDCS), pudendal nerve stimulation, multifidus muscle stimulation, transcutaneous electrical nerve stimulation (TENS), tibial nerve stimulation, among other peripheral nerve or organ stimulation. The IND 112 may additionally or alternatively provide therapies such as radiofrequency ablation (RFA), pulsed radiofrequency ablation, ultrasound therapy, high-intensity focused ultrasound (HIFU), optical stimulation, optogenetic therapy, magnetic stimulation, other peripheral tissue stimulation therapies, other peripheral tissue denervation therapies, or nerve blocks or injections.

In various examples, the electrodes 116 may be distributed in one or more leads of the lead system 114 electrically coupled to the IND 112. In an example, the lead system 114 may include a directional lead that includes at least some segmented electrodes circumferentially disposed about the directional lead. Two or more segmented electrodes may be distributed along a circumference of the lead. The actual number and shape of leads and electrodes may vary according to the intended application. Detailed description of construction and method of manufacturing percutaneous stimulation leads are disclosed in U.S. Pat. No. 8,019,439, entitled “Lead Assembly and Method of Making Same,” and U.S. Pat. No. 7,650,184, entitled “Cylindrical Multi-Contact Electrode Lead for Neural Stimulation and Method of Making Same,” the disclosures of which are incorporated herein by reference. The electrodes 116 may provide an electrically conductive contact providing for an electrical interface between the IND 112 and tissue of the patient. The neurostimulation pulses are each delivered from the IND 112 through a set of electrodes selected from the electrodes 116. In various examples, the neurostimulation pulses may include one or more individually defined pulses, and the set of electrodes may be individually definable by the user for each of the individually defined pulses.

Although the discussion herein with regard to the neuromodulation system 100 focuses on an implantable device such as the IND 112, this is meant only by way of example and not limitation. It is within the contemplation of the present inventors and within the scope of this document, that the systems, devices, and methods discussed herein may also be used for pain management via subcutaneous medical devices, wearable medical devices (e.g., wrist watches, patches, garment- or shoe-mounted devices, headgear, eye glasses, or earplugs), or other external medical devices, or a combination of implantable, wearable, or other external devices. The therapy, such as electrostimulation or medical therapies, may be used to treat various neurological disorders other than pain, which by way of example and not limitation may include epilepsy, migraine, Tourette's syndrome, obsessive compulsive disorder, tremor, Parkinson's disease, or dystonia, among other movement and affective disorders.

The external system 130 may be communicated with the IND 112 via a communication link 120. The external system 130 may include a dedicated hardware/software system such as a programmer, a remote server-based patient management system, or alternatively a system defined predominantly by software running on a standard personal computer. In some examples, at least a portion of the external system 130 may be ambulatory such as configured to be worn or carried by a subject. The external system 130 may be configured to control the operation of the IND 112, such as to program the IND 112 for delivering neuromodulation therapy. The external system 130 may additionally receive via the communication link 120 information acquired by IND 112, such as one or more physiological signals. In an example, the external system 130 may determine a pain score based on the physiological signals received from the IND 112, and program the IND 112 to deliver pain therapy in a closed-loop fashion. Examples of the external system and neurostimulation based on pain score are discussed below, such as with reference to FIGS. 2-3.

The communication link 120 may include one or more communication channels and intermediate devices between the external system and the IND, such as a wired link, a telecommunication link such as an internet connection, or a wireless link such as one or more of an inductive telemetry link, a radio-frequency telemetry link. The communication link 120 may provide for data transmission between the IND 112 and the external system 130. The transmitted data may include, for example, real-time physiological signals acquired by and stored in the IND 112, therapy history data, data indicating device operational status of the IND 112, one or more programming instructions to the IND 112 which may include configurations for sensing physiological signal or stimulation commands and stimulation parameters, or device self-diagnostic test, among others. In some examples, the IND 112 may be coupled to the external system 130 further via an intermediate control device, such as a handheld external remote control device to remotely instruct the IND 112 to generate electrical stimulation pulses in accordance with selected stimulation parameters produced by the external system 130, or to store the collected data into the external system 130.

Portions of the IND 112 or the external system 130 may be implemented using hardware, software, firmware, or combinations thereof. Portions of the IND 112 or the external system 130 may be implemented using an application-specific circuit that may be constructed or configured to perform one or more particular functions, or may be implemented using a general-purpose circuit that may be programmed or otherwise configured to perform one or more particular functions. Such a general-purpose circuit may include a microprocessor or a portion thereof, a microcontroller or a portion thereof, or a programmable logic circuit, or a portion thereof. For example, a “comparator” may include, among other things, an electronic circuit comparator that may be constructed to perform the specific function of a comparison between two signals or the comparator may be implemented as a portion of a general-purpose circuit that may be driven by a code instructing a portion of the general-purpose circuit to perform a comparison between the two signals.

FIG. 2 illustrates, by way of example and not limitation, a block diagram of a pain management system 200, which may be an embodiment of the neuromodulation system 100. The pain management system 200 may assess pain of a subject using at least one physiological signal, and program a pain therapy based on the pain assessment. As illustrated in FIG. 2, the pain management system 200 may include a sensor circuit 210, a pain analyzer circuit 220, a memory 230, a user interface 240, and a therapy unit 250.

The sensor circuit 210 may be coupled to one or more physiological sensors to sense from the patient at least one physiological signal. The sensor circuit 210 may include sense amplifier circuit that may pre-process the sensed physiological signals, including, for example, amplification, digitization, filtering, or other signal conditioning operations. Various physiological signals, such as cardiac, pulmonary, neural, or biochemical signals may demonstrate characteristic signal properties in response to an onset, intensity, severity, duration, or patterns of pain. In an example, the sensor circuit 210 may be coupled to implantable or wearable sensors to sense cardiac signals such as electrocardiograph (ECG), intracardiac electrogram, gyrocardiography, magnetocardiography, heart rate signal, heart rate variability signal, cardiovascular pressure signal, or heart sounds signal, among others. In another example, the sensor circuit 210 may sense pulmonary signals such as a respiratory signal, a thoracic impedance signal, or a respiratory sounds signal. In yet another example, the sensor circuit 210 may sense biochemical signals such as blood chemistry measurements or expression levels of one or more biomarkers, which may include, by way of example and not limitation, B-type natriuretic peptide (BNP) or N-terminal pro b-type natriuretic peptide (NT-proBNP), serum cytokine profiles, P2X4 receptor expression levels, gamma-aminobutyric acid (GABA) levels, TNFα and other inflammatory markers, cortisol, adenosine, Glial cell-derived neurotrophic factor (GDNF), Nav 1.3, Nav 1.7, or Tetrahydrobiopterin (BH4) levels, among other biomarkers.

In an example, the sensor circuit 210 may sense one or more physiological signals including a respiration signal and a heart rate signal. The physiological sensors may be an ambulatory sensor, such as an implantable or wearable sensor associated with the patient. In an example, the sensor circuit 210 may be coupled to a respiration sensor to sense the respiration signal, and coupled to a heart rate sensor to sense a heart rate signal to detect heart rates. The heart rate sensor may include one or more implantable, wearable, or otherwise ambulatory cardiac activity sensor configured to sense cardiac electrical or mechanical activity. In an example, the heart rate sensor is configured to sense an electrocardiogram (ECG) using surface electrodes or subcutaneous electrodes, or sense intracardiac electrograms (EGM) from inside the heart chamber or heart tissue using intracardiac electrodes. The heart rate sensor may alternatively or additionally include an accelerometer or a microphone configured to sense heart sounds (HS) signal in a patient, an impedance sensor configured to sense variations of intracardiac impedance as a result of cyclic cardiac contractions, or a pressure sensor configured to sense arterial pulses, among others.

The respiration sensor may be coupled to electrodes attached to or implanted in the patient to sense the respiration signal from the patient. The respiration signal includes a respiration waveform that represents the change of air flow or lung volume during a respiratory cycle. The respiration sensor may include a flowmeter configured to sense directly the air flow in the respiratory system or volume change in the lungs. Alternatively, the respiration sensor may sense a physiological signal modulated by respiration, such as a thoracic impedance signal. The thoracic impedance may be measured using electrodes on an implantable lead coupled to an implantable medical device. In an example, the thoracic impedance may be sensed between an electrode on a right ventricular and the can housing of and implantable device implanted at the left or right pectoral region. In another example, the thoracic impedance may be sensed between an electrode on a left ventricle and the can housing of the implantable device, or between a right atrium electrode and the can housing of the implantable device. The thoracic impedance may alternatively be measured using non-invasive surface electrodes removably attached to a patient chest. In some examples, the respiration sensors may sense other respiration-modulated physiological signals including, for example, chest muscle strain sensor to measure cyclic changes in muscle tension corresponding to respiration cycles, accelerometers to measure acceleration associated with displacement or movement of chest walls corresponding to respiration, or acoustic or vibrational signals modulated by respiration. The respiration sensors may alternatively include patient-external respiratory bands, respiration flowmeter, implantable or patient-external breath sound detector, blood oxygen level detector, and other sensors configured to sense a respiration-modulated physiological signal, which can be found in Lee et al., U.S. Pat. No. 7,678,061 entitled “System and method for characterizing patient respiration”, filed on Apr. 15, 2004, which is incorporated herein by reference in its entirety.

In another example, the sensor circuit 210 may be coupled to a physiological sensor to sense a physiological signal that is modulated by both cardiac rhythm and respiratory rhythm. The sensor circuit 210 may include at least first and second filter circuits. The first filter may filter the sensed physiological signal to generate a respiration signal, and the second filter may filter the sensed physiological signal generate the heart rate signal. The second filter circuit may have a higher center frequency than the first filter circuit. In an example, the heart rates and the respiration signal may be generated from a cardiac electrical signal, such as an electrocardiograph (ECG). During inspiration, the diaphragm shift downwards away from the apex of the heart. The increased filling of the lungs further stretch the apex of the heart towards the abdomen. During expiration, the lung volume reduces, and the diaphragm elevates upwards toward the heart which compresses the apex of the heart towards the breast. As a result, the angle of the electric cardiac vector that gives rise to the ECG signal changes during inspiratory and respiratory phases, which leads to cyclic variation in R-wave amplitude on the ECG signal. The respiration signal can be obtained from the R-wave amplitude signal using demodulation method. Other physiological signals, such as a heart sound signal, a blood pressure signal, a blood flow signal, a tissue strain signal, or a tissue impedance signal may each be modulated by cardiac rhythm and respiratory rhythm, and can be filtered using respective filters to extract the heart rate signal and the respiration signal.

The pain analyzer circuit 220 may generate a pain score using at least the physiological signals received from the sensor circuit 210. The pain analyzer circuit 220 may be implemented as a part of a microprocessor circuit, which may be a dedicated processor such as a digital signal processor, application specific integrated circuit (ASIC), microprocessor, or other type of processor for processing information including physical activity information. Alternatively, the microprocessor circuit may be a general purpose processor that may receive and execute a set of instructions of performing the functions, methods, or techniques described herein.

The pain analyzer circuit 220 may include circuit sets comprising one or more other circuits or sub-circuits that may, alone or in combination, perform the functions, methods or techniques described herein. In an example, hardware of the circuit set may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation. In connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa. The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuit set in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, the computer readable medium is communicatively coupled to the other components of the circuit set member when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuit set. For example, under operation, execution units may be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set, or by a third circuit in a second circuit set at a different time.

As illustrated in FIG. 2, the pain analyzer circuit 220 may include a respiration signal analyzer 221, signal metrics generator 222, and a pain score generator 225. The respiration signal analyzer 221 may be configured to detect from the sensed respiration signal a plurality of respiratory cycles, and determine for each respiratory cycle a respiratory cycle period. The respiratory cycle period may be determined as the duration between two time instants that represent the same state of respiration. For example, the respiratory cycle period may be computed as the duration between the end-of-expiration states. The respiration signal analyzer 221 may additionally be configured to detect, within each respiratory cycle, an inspiration phase and an expiration phase. The inspiration phase is a period between an end-of-expiration state and the next end-of-inspiration state. The expiration phase is a period between an end-of-inspiration state and the next end-of-expiration state. In an example where the respiration sensor directly or indirectly measures the lung volume, the end-of-expiration state may correspond to the minimal lung volume (or a metric indicative of the lung volume) within a specified detection window; and the end-of-inspiration state may correspond to the maximal lung volume (or a metric indicative of the lung volume) with a specified detection window. In another example where the respiration sensor senses thoracic impedance, the thoracic impedance increases when the air volume in the lungs increases (e.g., during inspiration). The end-of-expiration state may correspond to the minimal transthoracic impedance within a specified detection window; and the end-of-inspiration state may correspond to the maximal transthoracic impedance within a specified detection window.

The signal metrics generator 222 may generate one or more respiration-mediated heart rate metrics 223 using the heart rates detected from the heart rate signal and the respiration cycle or respiratory phases from the respiration signal. The detected heart rates, or cardiac cycles, may be synchronized to the respiratory cycles, inspiration phase, or expiration phase. The HR-respiration synchronization process can compensate both system lag and physiological lag between the respiration signal and the heart rate signal. The system lag includes the lag between the heart rate sensor and the respiration signal sensor in sensing and processing the respective signals. In an example, a system synchronization signal such as an electrical pulse may be issued and sensed by both the heart rate sensor and the respiration sensor, and the system lag may be determined as the time between the pulse signature on the heart rate signal and the pulse signature on the respiration signal. The physiological phase lag between HR and respiration signal may be due to the physiological response delay between heart rate and respiration. For example, because respiratory sinus arrhythmia (RSA) is associated with baroreflex, the HR change may lag behind the respiratory phase change. For example, during inspiration, thoracic pressure drops and the venous return increases, and the arterial baroreceptors are unloaded. Through the baroreflex mechanism, the vagus nerve activity is suppressed, which leads to an increase in sinus node firing and thus an increase in HR. The HR-respiration synchronization may compensate the physiological lag between HR and respiration signal.

The signal metrics generator 222 may generate one or more respiration-mediated heart rate metrics 223 using the respiration-synchronized heart rates. In an example, the respiration-mediated heart rate metrics 223 may indicate a variability of the heart rates (or cardiac cycles such as R-R intervals) within one or more respiratory cycles, hereinafter referred to as respiration-mediated heart rate variation (RM-HRV). The RM-HRV represents variability of heart rate (or cardiac cycle length) modulated by respiration, such as heart rate variability at different phases of respiration. A large RM-HRV (such as exceeding a threshold value) may be a result of persistence or restoration of automatic balance, which indicates no pain or an effective pain-relief effect. Conversely, a small RM-HRV (such as falling below a threshold value) may be caused by parasympathetic withdrawal, which indicates persistence of pain or ineffective pain therapy. Examples of the variability may include heart rate range within a respiratory cycle (e.g., a relative difference between the maximal heart rate and the minimal heart rate within the respiratory cycle), a statistical measure out of a histogram of the heart rates within a respiratory cycle, or a standard deviation, variance, or other spreaders measures of the heart rates within a respiratory cycle. In another example, the RM-HRV may be calculated as a relative difference (ΔHR_Insp-Exp) between a representative heart rates during the inspiration phase (HR_Insp) and a representative heart rate during the expiration phase (HR_Exp). The representative heart rates may be computed as a maximal value, a mean, a median, or a mode of the heart rates within the respective respiratory phases. A large difference ΔHR_Insp-Exp, such as when exceeding a threshold value, may be a result of persistence or restoration of automatic balance, and indicate no pain or effective pain-relief therapy. Conversely, a small difference ΔHR_Insp-Exp, such as when falling below a threshold value, may be caused by parasympathetic withdrawal, which indicates persistence of pain or ineffective pain therapy.

In some examples, the RM-HRV may be calculated as a heart rate variability during the inspiration phase (HRV_Insp) and/or a heart rate variability during the expiration phase (HRV_Exp). In an example, the HRV_Exp may be compared to a threshold such as a baseline HRV_Exp measured when the patient is known to be free of pain. If the HRV_Exp falls within a specified margin of the baseline HRV_Exp, it may indicate adequate maintenance or substantial restoration of automatic balance, and no pain or effective pain-relief therapy. Conversely, if the HRV_Exp is less than the baseline HRV_Exp by a specified margin, that is, substantial reduction of HRV during expiration, it may indicate parasympathetic withdrawal and persistence of pain or ineffective pain therapy. In another example, the RM-HRV may be calculated as a relative difference (ΔHRV_Insp-Exp) between the HRV_Insp and HRV_Exp. A large difference ΔHRV_Insp-Exp, such as when exceeding a threshold value, may be a result of persistence or restoration of automatic balance, which indicates no pain or effective pain-relief therapy. Conversely, a small difference ΔHRV_Insp-Exp, such as when falling below a threshold value, may indicate parasympathetic withdrawal and persistence of pain or ineffective pain-relief therapy.

The pain score generator 225 may generate a pain score using the measurements of the signal metrics generated by the signal metrics generator 222. The pain score may be represented as a numerical or categorical value that quantifies a patient overall pain symptom. In an example, a composite signal metric may be generated using a plurality of the signal metrics respectively weighted by weight factors. The combination may be linear or nonlinear. The pain score generator 225 may compare the composite signal metric to one or more threshold values or range values, and assign a corresponding pain score (such as numerical values from 0 to 10) based on the comparison.

In another example, the pain score generator 225 may compare the signal metrics to their respective threshold values or range values, assign corresponding signal metric-specific pain scores based on the comparison, and compute a composite pain score using a linear or nonlinear fusion of the signal metric-specific pain scores each weighted by their respective weight factor. In an example, the threshold may be inversely proportional to signal metric's sensitivity to pain. A signal metric that is more sensitive to pain may have a corresponding lower threshold and a larger metric-specific pain score, thus plays a more dominant role in the composite pain score than another signal metric that is less sensitive to pain. Examples of the fusion algorithm may include weighted averages, voting, decision trees, or neural networks, among others. The pain score generated by the pain score generator 225 may be output to a system user or a process.

In various examples, in addition to the physiological signals such as the respiration and heart rate signals, the sensor circuit 210 may sense one or more functional signals from the patient. Examples of the functional signals may include, but not limited to, patient posture, gait, balance, or physical activity signals, among others. The sensor circuit 210 may sense the functional signals via one or more implantable or wearable motion sensors, including an accelerometer, a gyroscope (which may be a one-, two-, or three-axis gyroscope), a magnetometer (e.g., a compass), an inclinometer, a goniometer, a electromagnetic tracking system (ETS), or a global positioning system (GPS) sensor, among others. Detailed description of functional signals for use in pain characterization are disclosed in commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,075, entitled “PAIN MANAGEMENT BASED ON FUNCTIONAL MEASUREMENTS”, the disclosures of which are incorporated herein by reference. The signal metrics generator 221 may generate functional signal metrics from the functional signals, and the pain score generator 225 may determine the pain score using a linear or nonlinear combination of the muscle tension signal metrics and the functional signal metrics. Commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,053, entitled “PAIN MANAGEMENT BASED ON CARDIOVASCULAR PARAMETERS” describes cardiovascular parameters such as arterial pulsatile activity and electrocardiography for use in pain analysis, the disclosure of which is incorporated herein by reference in its entirety. Commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,061, entitled “PAIN MANAGEMENT BASED ON BRAIN ACTIVITY MONITORING” describes information of brain activity for use in pain analysis, the disclosure of which is incorporated herein by reference in its entirety. Commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,061, entitled “PAIN MANAGEMENT BASED ON BRAIN ACTIVITY MONITORING” describes information of brain activity for use in pain analysis, the disclosure of which is incorporated herein by reference in its entirety. Commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,082, entitled “PAIN MANAGEMENT BASED ON EMOTIONAL EXPRESSION MEASUREMENTS” describes measurements of patient emotional expressions for use in pain analysis, the disclosure of which is incorporated herein by reference in its entirety. Commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,092, entitled “PAIN MANAGEMENT BASED ON MUSCLE TENSION MEASUREMENTS” describes measurements of patient muscle tension including electromyography for use in pain analysis, the disclosure of which is incorporated herein by reference in its entirety. One or more of these additional signals or measurements may be used by the pain analyzer circuit 220 to generate a pain score.

The memory 230 may be configured to store sensor signals or signal metrics such as generated by the sensor circuit 210 and the signal metrics generator 222, and the pain scores such as generated by the pain score generator 225. Data storage at the memory 230 may be continuous, periodic, or triggered by a user command or a specific event. In an example, as illustrated in FIG. 2, the memory 230 may store weight factors, which may be used by the pain score generator 225 to generate the composite pain score. The weight factors may be provided by a system user, or alternatively be automatically determined or adjusted such as based on the corresponding signal metrics' reliability in representing an intensity of the pain. Examples of the automatic weight factor generation are discussed below, such as with reference to FIG. 3.

The user interface 240 may include an input circuit 241 and an output unit 242. In an example, at least a portion of the user interface 240 may be implemented in the external system 130. The input circuit 241 may enable a system user to program the parameters used for sensing the physiological signals, generating signal metrics, or generating the pain score. The input circuit 241 may be coupled to one or more input devices such as a keyboard, on-screen keyboard, mouse, trackball, touchpad, touch-screen, or other pointing or navigating devices. In some example, the input device may be incorporated in a mobile device such as a smart phone or other portable electronic device configured to execute a mobile application (“App”). The mobile App may enable a patient to provide pain description or quantified pain scales during the pain episodes. In an example, the input circuit 241 may enable a user to confirm, reject, or edit the programming of the therapy unit 250, such as parameters for electrostimulation, as to be discussed in the following.

The output unit 242 may include a display to present to a system user such as a clinician the pain score. The output unit 242 may also display information including the physiological and functional signals, trends of the signal metric, or any intermediary results for pain score calculation such as the signal metric-specific pain scores. The information may be presented in a table, a chart, a diagram, or any other types of textual, tabular, or graphical presentation formats, for displaying to a system user. The presentation of the output information may include audio or other human-perceptible media format. In an example, the output unit 242 may generate alerts, alarms, emergency calls, or other forms of warnings to signal the system user about the pain score.

The therapy circuit 250 may be configured to deliver a therapy to the patient based on the pain score generated by the pain score generator 225. The therapy circuit 250 may include an electrostimulator configured to generate electrostimulation energy to treat pain. In an example, the electrostimulator may deliver spinal cord stimulation (SCS) via electrodes electrically coupled to the electrostimulator. The electrodes may be surgically placed at a region at or near a spinal cord tissue, which may include, by way of example and not limitation, dorsal column, dorsal horn, spinal nerve roots such as the dorsal nerve root, dorsal root entry zone, spinothalamic tract, and dorsal root ganglia. The SCS may be in a form of stimulation pulses that are characterized by pulse amplitude, pulse width, stimulation frequency, duration, on-off cycle, pulse shape or waveform, temporal pattern of the stimulation, among other stimulation parameters. Examples of the stimulation pattern may include burst stimulation with substantially identical inter-pulse intervals, or ramp stimulation with incremental inter-pulse intervals or with decremental inter-pulse intervals. In some examples, the frequency or the pulse width may change from pulse to pulse. The electrostimulator may additionally or alternatively deliver electrostimulation to other target tissues such as peripheral nerves tissues. In an example, the electrostimulator may deliver transcutaneous electrical nerve stimulation (TENS) via detachable electrodes that are affixed to the skin.

The therapy circuit 250 may additionally or alternatively include a drug delivery system, such as an intrathecal drug delivery pump that may be surgically placed under the skin, which may be programmed to inject medication or biologics through a catheter to the area around the spinal cord. Other examples of drug delivery system may include a computerized patient-controlled analgesia pump that may deliver the prescribed pain medication to the patient such as via an intravenous line. In some examples, the therapy circuit 250 may be delivered according to the pain score received from the pain score generator 225.

FIG. 3 illustrates, by way of example and not limitation, a block diagram of another example of a pain management system 300, which may be an embodiment of the neuromodulation system 100 or the pain management system 200. The pain management system 300 may include an implantable neuromodulator 310 and an external system 320, which may be, respectively, embodiments of the IND 112 and the external system 130 as illustrated in FIG. 1. The external system 320 may be communicatively coupled to the implantable neuromodulator 310 via the communication link 120.

The implantable neuromodulator 310 may include several components of the pain management system 200 as illustrated in FIG. 2, including the sensor circuit 210, the pain analyzer circuit 220, the memory 230, and the therapy unit 250. As discussed with reference to FIG. 2, the pain analyzer circuit 220 includes the pain score generator 225 that determine a pain score using weight factors stored in the memory 230 and the signal metrics from the signal metrics generator 222 which may also be included in the pain analyzer circuit 220. The implantable neuromodulator 310 may include a controller 312, coupled to the therapy unit 250, that controls the generation and delivery of pain therapy, such as neurostimulation energy. The controller 312 may control the generation of electrostimulation pulses according to specific stimulation parameters. The stimulation parameters may be provided by a system user. Alternatively, the stimulation parameters may be automatically determined based on the intensity, severity, duration, or pattern of pain, which may be subjectively described by the patient or automatically quantified based on the physiological signals sensed by the sensor circuit 210. For example, when a patient-described or sensor-indicated quantification exceeds a respective threshold value or falls within a specific range indicating elevated pain, the electrostimulation energy may be increased to provide stronger pain relief. Increased electrostimulation energy may be achieved by programming a higher pulse intensity, a higher frequency, or a longer stimulation duration or “on” cycle, among others. Conversely, when a patient-described or sensor-indicated pain quantification falls below a respective threshold value or falls within a specific range indicating no pain or mild pain, the electrostimulation energy may be decreased. The controller circuit 312 may also adjust stimulation parameters to alleviate side effects introduced by the electrostimulation of the target tissue.

Additionally or alternatively, the controller circuit 312 may control the therapy unit 250 to deliver electrostimulation pulses via specific electrodes. In an example of pain management via SCS, a plurality of segmented electrodes, such as the electrodes 116, may be distributed in one or more leads. The controller circuit 312 may configure the therapy unit 250 to deliver electrostimulation pulses via a set of electrodes selected from the plurality of electrodes. The electrodes may be manually selected by a system user or automatically selected based on the pain score.

The implantable neuromodulator 310 may receive the information about electrostimulation parameters and the electrode configuration from the external system 320 via the communication link 120. Additional parameters associated with operation of the therapy unit 250, such as battery status, lead impedance and integrity, or device diagnostic of the implantable neuromodulator 310, may be transmitted to the external system 320. The controller circuit 312 may control the generation and delivery of electrostimulation using the information about electrostimulation parameters and the electrode configuration from the external system 320. Examples of the electrostimulation parameters and electrode configuration may include: temporal modulation parameters such as pulse amplitude, pulse width, pulse rate, or burst intensity; morphological modulation parameters respectively defining one or more portions of stimulation waveform morphology such as amplitude of different phases or pulses included in a stimulation burst; or spatial modulation parameters such as selection of active electrodes, electrode combinations which define the electrodes that are activated as anodes (positive), cathodes (negative), and turned off (zero), and stimulation energy fractionalization which defines amount of current, voltage, or energy assigned to each active electrode and thereby determines spatial distribution of the modulation field.

In an example, the controller circuit 312 may control the generation and delivery of electrostimulation in a closed-loop fashion by adaptively adjusting one or more stimulation parameters or stimulation electrode configuration based on the pain score. For example, if the score exceeds the pain threshold (or falls within a specific range indicating an elevated pain), then the first electrostimulation may be delivered. Conversely, if the composite pain score falls below a respective threshold value (or falls within a specific range indicating no pain or mild pain), then a second pain therapy, such as second electrostimulation may be delivered. The first electrostimulation may differ from the second electrostimulation with respect to at least one of the stimulation energy, pulse amplitude, pulse width, stimulation frequency, duration, on-off cycle, pulse shape or waveform, electrostimulation pattern such as electrode configuration or energy fractionalization among active electrodes, among other stimulation parameters. In an example, the first electrostimulation may have higher energy than the second electrostimulation, such as to provide stronger effect of pain relief. Examples of increased electrostimulation energy may include a higher pulse intensity, a higher frequency, or a longer stimulation duration or “on” cycle, among others.

The parameter adjustment or stimulation electrode configuration may be executed continuously, periodically at specific time, duration, or frequency, or in a commanded mode upon receiving from a system user a command or confirmation of parameter adjustment. In some examples, the closed-loop control of the electrostimulation may be further based on the type of the pain, such as chronic or acute pain. In an example, the pain analyzer circuit 220 may trend the signal metric over time to compute an indication of abruptness of change of the signal metrics, such as a rate of change over a specific time period. The pain episode may be characterized as acute pain if the signal metric changes abruptly (e.g., the rate of change of the signal metric exceeding a threshold), or as chronic pain if the signal metric changes gradually (e.g., the rate of change of the signal metric falling below a threshold). The controller circuit 312 may control the therapy unit 250 to deliver, withhold, or otherwise modify the pain therapy in accordance with the pain type. For example, incidents such as toe stubbing or bodily injuries may cause abrupt changes in certain signal metrics, but no adjustment of the closed-loop pain therapy is deemed necessary. On the contrary, if the pain analyzer circuit 220 detects chronic pain characterized by gradual signal metric change, then the closed-loop pain therapy may be delivered accordingly.

The external system 320 may include the user interface 240, a weight generator 322, and a programmer circuit 324. The weight generator 322 may generate weight factors used by the pain score generator 225 to generate the pain score. The weight factors may indicate the signal metrics' reliability in representing an intensity of the pain. A sensor metric that is more reliable, or more sensitive or specific to the pain, would be assigned a larger weight than another sensor metric that is less reliable, or less sensitive or specific to the pain. In an example, the weight factors may be proportional to correlations between a plurality of quantified pain scales (such as reported by a patient) and measurements of the signal metrics corresponding to the plurality of quantified pain scales. A signal metric that correlates with the pain scales is deemed a more reliable signal metric for pain quantification, and is assigned a larger weight factor than another signal metric less correlated with the quantified pain scales. In another example, the weight generator 322 may determine weight factors using the signal sensitivity to pain. The signal metrics may be trended over time, such as over approximately six months. The signal sensitivity to pain may be represented by a rate of change of the signal metrics over time during a pain episode. The signal sensitivity to pain may be evaluated under a controlled condition such as when the patient posture or activity is at a specific level or during specific time of the day. The weight generator 322 may determine weight factors to be proportional to signal metric's sensitivity to pain.

The programmer circuit 324 may produce parameter values for operating the implantable neuromodulator 310, including parameters for sensing physiological signals and generating signal metrics, and parameters or electrode configurations for electrostimulation. In an example, the programmer circuit 324 may generate the stimulation parameters or electrode configurations for SCS based on the pain score produced by the pain score generator 225. Through the communication link 120, the programmer circuit 324 may continuously or periodically provide adjusted stimulation parameters or electrode configuration to the implantable neuromodulator 210. By way of non-limiting example and as illustrated in FIG. 3, the programmer circuit 324 may be coupled to the user interface 234 to allow a user to confirm, reject, or edit the stimulation parameters, sensing parameters, or other parameters controlling the operation of the implantable neuromodulator 210. The programmer circuit 324 may also adjust the stimulation parameter or electrode configuration in a commanded mode upon receiving from a system user a command or confirmation of parameter adjustment.

The programmer circuit 324, which may be coupled to the weight generator 322, may initiate a transmission of the weight factors generated by the weight generator 322 to the implantable neuromodulator 310, and store the weight factors in the memory 230. In an example, the weight factors received from the external system 320 may be compared to previously stored weight factors in the memory 230. The controller circuit 312 may update the weight factors stored in the memory 230 if the received weight factors are different than the stored weights. The pain analyzer circuit 220 may use the updated weight factors to generate a pain score. In an example, the update of the stored weight factors may be performed continuously, periodically, or in a commanded mode upon receiving a command from a user. In various examples, weight factors may be updated using a fusion model. Commonly assigned U.S. Provisional Patent Application Ser. No. 62/445,095, entitled “PATIENT-SPECIFIC CALIBRATION OF PAIN QUANTIFICATION” describes systems and methods for calibrating a fusion model, such as adjusting weights for signal metrics, using a reference pain quantification, the disclosure of which is incorporated herein by reference in its entirety.

In some examples, the pain score may be used by a therapy unit (such as an electrostimulator) separated from the pain management system 300. In various examples, the pain management system 300 may be configured as a monitoring system for pain characterization and quantification without delivering closed-loop electrostimulation or other modalities of pain therapy. The pain characterization and quantification may be provided to a system user such as the patient or a clinician, or to a process including, for example, an instance of a computer program executable in a microprocessor. In an example, the process includes computer-implemented generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimens. The therapy recommendations or alert may be based on the pain score, and may be presented to the patient or the clinician in various settings including in-office assessments (e.g. spinal cord stimulation programming optimization), in-hospital monitoring (e.g. opioid dosing during surgery), or ambulatory monitoring (e.g. pharmaceutical dosing recommendations).

In an example, in response to the pain score exceeding a threshold which indicates elevated pain symptom, an alert may be generated and presented at the user interface 240 to remind the patient to take pain medication. In another example, therapy recommendations or alerts may be based on information about wearing-off effect of pain medication, which may be stored in the memory 230 or received from the user interface 240. When the drug effect has worn off, an alert may be generated to remind the patient to take another dose or to request a clinician review of the pain prescription. In yet another example, before a pain therapy such as neurostimulation therapy is adjusted (such as based on the pain score) and delivered to the patient, an alert may be generated to forewarn the patient or the clinician of any impending adverse events. This may be useful as some pain medication may have fatal or debilitating side effects. In some examples, the pain management system 300 may identify effect of pain medication addiction such as based on physiological signals. An alert may be generated to warn the patient about effects of medication addiction and thus allow medical intervention.

In some examples, the pain analyzer circuit 220 may be alternatively included in the external system 320. The pain analyzer circuit 220, or a portion of the pain analyzer circuit 220 such as one or more the respiration signal analyzer 221, signal metrics generator 222, or the pain score generator 225, may be included in a wearable device configured to be worn or carried by a subject. At least a portion of the sensor circuit 210 may also be included in the external system 320. A clinician may use the external system 320 to program the implantable neuromodulator 310 with appropriate pain therapy based on the pain score generated at the external system 320, such as during a clinical trial or patient follow-up visit at the clinic.

FIGS. 4A-B illustrates, by way of example and not limitation, diagrams of a comparison of respiration-mediated heart rates in a pain state (420 and 460) and respiration-mediated heart rates in a pain-free state (410 and 450). FIG. 4A illustrates, in a Cartesian coordinate system, a scatter plot of R wave-to-R wave intervals (RR intervals) within a respiratory cycle. The RR intervals may be computed from an ECG signal synchronized to a respiration signal. In a pain-free state or an effective pain therapy state, as illustrated in 410, the RR intervals 411 demonstrate a prominent pattern of cyclic variation with respiratory phases within a respiratory cycle. The average RR interval, represented by the trend line 412, is shorter (i.e., faster heart rate) during inspiration than the average RR interval during expiration (i.e., slower heart rate). The RR intervals during inspiration is less variable (as represented by a relatively smaller variability 413) than the RR intervals during expiration (as represented by a relatively larger variability 414). In a pain state or an ineffective pain therapy state, as illustrated in 420, the within—respiratory cycle cyclic variation pattern of the RR intervals 421 at different respiratory phases becomes less prominent. The average RR interval (represented by the trend line 422) is not substantially shorter during inspiration than during expiration. The RR interval variability during inspiration (as represented by a small variability 423) is not substantially smaller than the RR interval variability during expiration (as represented by a large variability 424). Additionally, the RR interval variability during expiration in a pain state (424) is substantially smaller than the RR interval variability during expiration in a pain-free state (414). The different patterns of average HR or HRV at various respiratory phases may be used to characterize and quantify the patient pain.

FIG. 4B illustrates a scatter plot of RR intervals 421 within a respiratory cycle in a polar coordinate system. Each respiration-synchronized RR interval is represented by a point in the polar coordinate system, as defined by a radius r and an angle θ. The radius r represents the RR interval (e.g., in milliseconds), and the angle θ represents the respiratory phase angle between 0 and 360 degrees. The plot 450 corresponds to the pain-free state or effective pain therapy state and the plot 460 corresponds to the pain state or ineffective pain therapy state. The upper quadrants of 450 and 460 (corresponding to 0<θ<180°) represents the inspiration phase. The lower quadrants of 450 and 460 (corresponding to 180°<θ<360°) represents the expiration phase. The RR intervals 451 in the polar coordinate system correspond to the RR intervals 411 in the Cartesian coordinate system, and the RR intervals 461 in the polar coordinate system correspond to the RR intervals 421 in the Cartesian coordinate system. As illustrated in 450, in the pain-free or effective pain therapy state, the average RR interval (represented by 452) during inspiration (the upper quadrants) is smaller than the average RR interval during expiration (the lower quadrants). This results in an asymmetrical average HR curve 452 about the horizontal axis that separates the inspiration phase from the expiration phase. In contrast, in the pain state or ineffective pain therapy state as illustrated in 460, the average RR interval (represented by 462) during inspiration (the upper quadrants) is close to the average RR interval during expiration (the lower quadrants). This results in a more symmetrical average HR curve 462 about the horizontal axis. Additionally, in pain-free or effective pain therapy state, the RR interval variability 453 during inspiration is substantially smaller than the RR interval variability 454 during expiration. However, such a respiratory phase-dependent distinction in RR interval variability becomes less prominent in pain state or ineffective pain therapy state, as indicated by the RR interval variability 463 during inspiration and the RR interval variability 464 during expiration. The graphical features, such as the degree of symmetry of the average RR interval trend 452 or 462, may be extracted from the plots 450 or 460 and used for generating the pain score.

FIG. 5 illustrates, by way of example and not limitation, a method 500 for managing pain of a patient. The method 500 may be implemented in a medical system, such as the pain management system 200 or 300. In an example, at least a portion of the method 500 may be executed by a neuromodulator device (IND) such as the implantable neuromodulator 310. In an example, at least a portion of the method 500 may be executed by an external programmer or remote server-based patient management system, such as the external system 320 that are communicatively coupled to the IND. The method 500 may be used to provide neuromodulation therapy to treat chronic pain or other disorders.

The method 500 begins at step 510, where a respiration signal and a heart rate signal may be sensed from the patient. The respiration may be sensed using a sensor circuit, such as the sensor circuit 210, that is coupled to one or more respiration sensors to sense an airflow signal in the respiratory system, chest volume signal, thoracic impedance signal, chest muscle tension, acceleration, or displacement signal, breath sound signal, or blood oxygen level signal, among other respiration-modulated physiological signals. The heart rate signal may be sensed using a sensor circuit, such as the sensor circuit 210, that is coupled to one or more electrodes or sensors to sense cardiac electrical or mechanical signals, such as an electrocardiogram, a intracardiac electrogram, a heart sounds signal, a pressure pulse signal, or an intracardiac impedance signal, among others.

In some examples, a physiological signal sensed at 510 may be modulated by both heart rhythm and respiratory rhythm. The sensed physiological signal may be filtered into components at different frequency bands using at least first and second filters. The first filter may filter the sensed physiological signal to generate a respiration signal, and the second filter may filter the sensed physiological signal generate the heart rate signal. The second filter circuit may have a higher center frequency than the first filter circuit. For example, an electrocardiograph (ECG) may be used to extract both the heart rates and the respiration signal. The angle of the electric cardiac vector that gives rise to the ECG signal changes during inspiration and respiratory phases. This may result in cyclic variation in R-wave amplitude on the ECG signal. The respiration signal can be obtained from the R-wave amplitude signal through a low-pass or pass-pass filter, or other demodulation method. In other examples, a heart sounds (HS) signal, a blood pressure signal, a blood flow signal, a tissue strain signal, a tissue impedance signal, or a nerve electrical signal may each be modulated by cardiac rhythm and respiratory rhythm, and can be filtered using respective filters to extract a heart rate signal and a respiration signal.

At 520, one or more signal metrics indicative of respiration-mediated heart rate variation using the sensed respiration signal and the heart rate signal. The respiration-medicate heart rate variation represents changes or pattern of changes of heart rates at different phases of a respiratory cycle. The sensed respiration signal may be processed to determine a respiratory cycle period, such as measured as a duration between two time instants that represent the same state of respiration. Within each respiratory cycle, an inspiration phase and an expiration phase may be detected.

Heart rates, or cardiac cycles, from the heart rate signal may be synchronized to the respiratory cycles, inspiration phase, or expiration phase. The HR-respiration synchronization process can compensate both system lag and physiological lag between the respiration signal and the heart rate signal. One or more respiration-mediated heart rate metrics may then be generated using the respiration-synchronized heart rates. In an example, the respiration-mediated heart rate metrics include a variance or a spreadness measure of the heart rates (or cardiac cycles such as R-R intervals) within one or more respiratory cycles, hereinafter referred to as respiration-mediated heart rate variation (RM-HRV). A large RM-HRV may correspond to maintained or restored automatic balance, which indicates no pain or effective therapy in relieving pain. A small RM-HRV may correspond to parasympathetic withdrawal, which indicates persistence of pain or ineffective pain therapy. In another example, the RM-HRV may be calculated as a relative difference between the heart rates during the inspiration phase and the heart rates during the expiration phase. A large difference in HR during respective respiration phases corresponds to persistence or restoration of automatic balance, which indicates no pain or effective therapy in relieving pain, and a small HR difference corresponds to parasympathetic withdrawal, which indicates persistence of pain or ineffective pain therapy. The RM-HRV may alternatively be calculated as a heart rate variability HRV_Insp during the inspiration phase, a heart rate variability HRV_Exp during the expiration phase, or a relative difference ΔHRV_Insp-Exp between the HRV_Insp and HRV_Exp. One or more of the HRV_Insp, the HRV_Exp, or the ΔHRV_Insp-Exp may be compared to their respective threshold values to determine the pain state or a pain-relief effect of a therapy.

At 530, a pain score may be generated using the measurements of the signal metrics such as one or more signal metrics indicative of respiration-mediated heart rate variation. The pain score may be represented as a numerical or categorical value that quantifies overall pain quality in the subject. In an example, a composite signal metric may be generated using a combination of the signal metrics each weighted by their respective weight factor. The composite signal metric may be categorized as one of a number of degrees of pain by comparing the composite signal metric to one or more threshold values or range values, and a corresponding pain score may be assigned based on the comparison. In another example, the signal metrics may be compared to their respective threshold values or range values and a corresponding signal metric-specific pain score may be determined. A composite pain score may be generated using a linear or nonlinear fusion of the signal metric-specific pain scores each weighted by their respective weight factor. In some examples, the pain score may be computed using a subset of the signal metrics selected based on their temporal profile of pain response. Signal metrics with quick pain response (or a shorter transient state of response) may be selected to compute the pain score during a pain episode. Signal metrics with slow or delayed pain response (or a longer transient state of response before reaching a steady state) may be used to compute the pain score after an extended period following the onset of pain such as to allow the signal metrics to reach steady state of response.

At 542, the pain score may be output to a user or to a process, such as via the output unit 242 as illustrated in FIG. 2. The pain score, including the composite pain score and optionally together with metric-specific pain scores, may be displayed on a display screen. Other information such as the respiration signal and the heart rate signal, and the signal metrics indicative of respiration-mediated heart rate variation, may also be output for display or for further processing. In some examples, alerts, alarms, emergency calls, or other forms of warnings may be generated to signal the system user about occurrence of a pain episode or aggravation of pain as indicated by the pain score.

The method 500 may include, at 544, an additional step of delivering a pain therapy to the patient according to the pain score. The pain therapy may include electrostimulation therapy, such as spinal cord stimulation (SCS) via electrodes electrically coupled to the electrostimulator. The SCS may be in a form of stimulation pulses that are characterized by pulse amplitude, pulse width, stimulation frequency, duration, on-off cycle, waveform, among other stimulation parameters. Other electrostimulation therapy, such as one or a combination of DBS, FES, VNS, TNS, or PNS at various locations, may be delivered for pain management. The pain therapy may additionally or alternatively include a drug therapy such as delivered by using an intrathecal drug delivery pump.

In various examples, the pain therapy (such as in the form of electrostimulation or drug therapy) may be delivered in a closed-loop fashion. Therapy parameters, such as stimulation waveform parameters, stimulation electrode combination and fractionalization, drug dosage, may be adaptively adjusted based at least on the pain score. The pain-relief effect of the delivered pain therapy may be assessed based on the signal metrics such as the cardiovascular parameters, and the therapy may be adjusted to achieve desirable pain relief. The therapy adjustment may be executed continuously, periodically at specific time, duration, or frequency, or in a commanded mode upon receiving from a system user a command or confirmation of parameter adjustment. In an example, if the pain score exceeds the pain threshold (or falls within a specific range indicating an elevated pain), then the first electrostimulation may be delivered. Conversely, if the composite pain score falls below a respective threshold value (or falls within a specific range indicating no pain or mild pain), then a second pain therapy, such as second electrostimulation may be delivered. The first electrostimulation may differ from the second electrostimulation with respect to at least one of the stimulation energy, pulse amplitude, pulse width, stimulation frequency, duration, on-off cycle, pulse shape or waveform, electrostimulation pattern such as electrode configuration or energy fractionalization among active electrodes, among other stimulation parameters. The method 500 may proceed at 510 to sense the respiration signal and the heart rate signal in response to the therapy delivered at 544. In some examples, the responses of the signal metrics to pain therapy delivered at 544 may be used to gauge composite pain score computation such as by adjusting the weight factors. In an example, weight factors may be determined and adjusted via the weight generator 322 as illustrated in FIG. 3, to be proportional to signal metric's sensitivity to pain.

FIG. 6 illustrates generally a block diagram of an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform. Portions of this description may apply to the computing framework of various portions of the LCP device, the IND, or the external programmer.

In alternative embodiments, the machine 600 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 600 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 600 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine 600 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate by, logic or a number of components, or mechanisms. Circuit sets are a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic, etc.). Circuit set membership may be flexible over time and underlying hardware variability. Circuit sets include members that may, alone or in combination, perform specific operations when operating. In an example, hardware of the circuit set may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation. In connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa. The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuit set in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, the computer readable medium is communicatively coupled to the other components of the circuit set member when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuit set. For example, under operation, execution units may be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set, or by a third circuit in a second circuit set at a different time.

Machine (e.g., computer system) 600 may include a hardware processor 602 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 604 and a static memory 606, some or all of which may communicate with each other via an interlink (e.g., bus) 608. The machine 600 may further include a display unit 610 (e.g., a raster display, vector display, holographic display, etc.), an alphanumeric input device 612 (e.g., a keyboard), and a user interface (UI) navigation device 614 (e.g., a mouse). In an example, the display unit 610, input device 612 and UI navigation device 614 may be a touch screen display. The machine 600 may additionally include a storage device (e.g., drive unit) 616, a signal generation device 618 (e.g., a speaker), a network interface device 620, and one or more sensors 621, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine 600 may include an output controller 628, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

The storage device 616 may include a machine readable medium 622 on which is stored one or more sets of data structures or instructions 624 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 624 may also reside, completely or at least partially, within the main memory 604, within static memory 606, or within the hardware processor 602 during execution thereof by the machine 600. In an example, one or any combination of the hardware processor 602, the main memory 604, the static memory 606, or the storage device 616 may constitute machine readable media.

While the machine readable medium 622 is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions 624.

The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 600 and that cause the machine 600 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a massed machine readable medium comprises a machine readable medium with a plurality of particles having invariant (e.g., rest) mass. Accordingly, massed machine-readable media are not transitory propagating signals. Specific examples of massed machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 624 may further be transmitted or received over a communications network 626 using a transmission medium via the network interface device 620 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as WiFi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device 620 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network 626. In an example, the network interface device 620 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 600, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

Various embodiments are illustrated in the figures above. One or more features from one or more of these embodiments may be combined to form other embodiments.

The method examples described herein can be machine or computer-implemented at least in part. Some examples may include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device or system to perform methods as described in the above examples. An implementation of such methods may include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code may include computer readable instructions for performing various methods. The code can form portions of computer program products. Further, the code can be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times.

The above detailed description is intended to be illustrative, and not restrictive. The scope of the disclosure should, therefore, be determined with references to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. A system for managing pain of a patient, the system comprising:

a sensor circuit coupled to one or more physiological sensors and configured to sense a respiration signal and a heart rate signal from the patient;

a pain analyzer circuit coupled to the sensor circuit, the pain analyzer circuit configured to:

generate one or more signal metrics indicative of respiration-mediated heart rate variation using the sensed respiration signal and the heart rate signal; and

generate a pain score using a combination of the generated one or more signal metrics each weighted by a respective weight factor; and

an output unit configured to output the pain score to a user or a process.

2. The system of claim 1, further comprising:

an electrostimulator configured to generate electrostimulation energy to treat pain; and

a controller circuit coupled to the pain analyzer circuit and the electrostimulator, the controller circuit configured to control the electrostimulator to deliver a pain therapy and to control the electrostimulation energy generated by the electrostimulator according to the pain score.

3. The system of claim 2, wherein the electrostimulator is further configured to deliver at least one of:

a spinal cord stimulation;

a brain stimulation; or

a peripheral nerve stimulation.

4. The system of claim 2, wherein:

the controller circuit is further configured to deliver first electrostimulation to the patient in response to the pain score exceeding a threshold value, and to deliver second electrostimulation to the patient in response to the pain score falling below the threshold value; and

the first electrostimulation differs from the second electrostimulation with respect to at least one of an electrostimulation energy, an electrostimulation pulse shape, or an electrostimulation pattern.

5. The system of claim 1, further comprising a respiratory phase detector configured to detect a plurality of respiratory cycles and at least one of an inspiration phase or an expiration phase in each of the plurality of respiratory cycles,

wherein the pain analyzer circuit is further configured to synchronize heart rates from the heart rate signal to the plurality of respiratory cycles, and to determine the respiration-mediated heart rate variation using the synchronized heart rates.

6. The system of claim 5, wherein the respiration-mediated heart rate variation indicates a difference between heart rates during the inspiration phase and heart rates during the expiration phase.

7. The system of claim 5, wherein the respiration-mediated heart rate variation indicates a variability of the synchronized heart rates within one or more of the plurality of respiratory cycles.

8. The system of claim 5, wherein the respiration-mediated heart rate variation includes at least one of:

a heart rate variability during the inspiration phase;

a heart rate variability during the expiration phase; or

a difference between the heart rate variability during the inspiration phase and the heart rate variability during the expiration phase.

9. The system of claim 1, wherein the sensor circuit is further configured to:

sense a physiological signal using at least one of the one or more physiological sensors; and

filter the sensed physiological signal, via a first filter circuit, to generate the respiration signal, or filter the sensed physiological signal, via a second filter circuit, to generate the heart rate signal;

wherein the second filter circuit uses a center frequency higher than a center frequency used by the first filter circuit.

10. The system of claim 9, wherein the sensor circuit is further configured to sense the physiological signal including at least one of:

a cardiac electrical signal;

a heart sound signal;

a blood pressure signal;

a blood flow signal;

a tissue strain signal; or

a tissue impedance signal.

11. The system of claim 2, further comprising an implantable neuromodulator device (IND) that includes one or more of the sensor circuit, the pain analyzer circuit, or the electrostimulator.

12. A method for managing pain of a patient using an implantable neuromodulator device (IND), the method comprising:

sensing a respiration signal and a heart rate signal from the patient via a sensor circuit;

generating one or more signal metrics indicative of respiration-mediated heart rate variation using the sensed respiration signal and the heart rate signal;

generating a pain score using a combination of the generated one or more signal metrics each weighted by a respective weight factor; and

outputting the pain score to a user or a process.

13. The method of claim 12, further comprising delivering a pain therapy via the IND, the pain therapy including electrostimulation energy determined according to the pain score.

14. The method of claim 12, further comprising:

detecting a plurality of respiratory cycles and at least one of an inspiration phase or an expiration phase in each of the plurality of respiratory cycles;

synchronizing heart rates from the heart rate signal to the plurality of respiratory cycles; and

determining the respiration-mediated heart rate variation using the synchronized heart rates.

15. The method of claim 14, wherein the respiration-mediated heart rate variation indicates a difference between heart rates during the inspiration phase and heart rates during the expiration phase.

16. The method of claim 14, wherein the respiration-mediated heart rate variation indicates a variability of the synchronized heart rates within one or more of the plurality of respiratory cycles.

17. The method of claim 14, wherein the respiration-mediated heart rate variation includes at least one of:

a heart rate variability during the inspiration phase;

a heart rate variability during the expiration phase; or

a difference between the heart rate variability during the inspiration phase and the heart rate variability during the expiration phase.

18. The method of claim 12, wherein sensing the respiration signal and the heart rate signal includes:

sensing a physiological signal using a physiological sensor; and

filtering the sensed physiological signal, via a first filter circuit, to generate the respiration signal, or filtering the sensed physiological signal, via a second filter circuit, to generate the heart rate signal, wherein the second filter circuit uses a center frequency higher than a center frequency used by the first filter circuit.

19. At least one non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to:

receive a respiration signal and a heart rate signal sensed from a patient;

generate a plurality of signal metrics from the received two or more signals;

generate one or more signal metrics indicative of respiration-mediated heart rate variation using the sensed respiration signal and the heart rate signal;

generate a pain score using a combination of the generated one or more signal metrics each weighted by a respective weight factor; and

output the pain score to a user or a process.

20. The at least one non-transitory machine-readable medium of claim 19, wherein the instructions, when performed by the machine, cause the machine to:

detect a plurality of respiratory cycles and at least one of an inspiration phase or an expiration phase in each of the plurality of respiratory cycles;

synchronize heart rates from the heart rate signal to the plurality of respiratory cycles; and

determine the respiration-mediated heart rate variation using the synchronized heart rates.